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Associate Data Scientist Model Development
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- Education: Bachelor’s or master’s degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related discipline. A master’s is a plus.
- Experience: 2 - 3 years of relevant real-world experience in a data science or machine learning role, preferably within the financial services industry.
- Technical Skills:
- Proficiency in programming languages, particularly Python, and experience with relevant data science libraries (e.g., NumPy, pandas, scikit-learn, TensorFlow/PyTorch).
- Strong knowledge of SQL for database querying and data extraction.
- Familiarity with big data technologies and cloud platforms (e.g., Spark, Hadoop, AWS, Azure, GCP).
- Experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib) for creating reports and dashboards.
- Solid understanding of statistical concepts, machine learning theory, algorithms, and probability.
- Soft Skills:
- Strong analytical, critical thinking, and problem-solving abilities.
- Excellent written and oral communication skills, with the ability to explain technical concepts to a non-technical audience.
- Proactive and self-motivated with the ability to work in a fast-paced, collaborative team environment.
- Strong attention to detail and ability to work with incomplete information.
- Preferred Qualifications
- Experience with MLOps platforms and tools (e.g., MLflow, Kubeflow, Docker, Kubernetes).
- Knowledge of specific financial domain areas such as credit scoring, fraud detection, or portfolio optimization.
- Familiarity with Natural Language Processing (NLP) techniques and Generative AI workflows.
- Education: Bachelor’s or master’s degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related discipline. A master’s is a plus.
- Experience: 2 - 3 years of relevant real-world experience in a data science or machine learning role, preferably within the financial services industry.
- Technical Skills:
- Proficiency in programming languages, particularly Python, and experience with relevant data science libraries (e.g., NumPy, pandas, scikit-learn, TensorFlow/PyTorch).
- Strong knowledge of SQL for database querying and data extraction.
- Familiarity with big data technologies and cloud platforms (e.g., Spark, Hadoop, AWS, Azure, GCP).
- Experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib) for creating reports and dashboards.
- Solid understanding of statistical concepts, machine learning theory, algorithms, and probability.
- Soft Skills:
- Strong analytical, critical thinking, and problem-solving abilities.
- Excellent written and oral communication skills, with the ability to explain technical concepts to a non-technical audience.
- Proactive and self-motivated with the ability to work in a fast-paced, collaborative team environment.
- Strong attention to detail and ability to work with incomplete information.
- Preferred Qualifications
- Experience with MLOps platforms and tools (e.g., MLflow, Kubeflow, Docker, Kubernetes).
- Knowledge of specific financial domain areas such as credit scoring, fraud detection, or portfolio optimization.
- Familiarity with Natural Language Processing (NLP) techniques and Generative AI workflows.
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